WO2013179359A1 - 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム - Google Patents

移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム Download PDF

Info

Publication number
WO2013179359A1
WO2013179359A1 PCT/JP2012/003624 JP2012003624W WO2013179359A1 WO 2013179359 A1 WO2013179359 A1 WO 2013179359A1 JP 2012003624 W JP2012003624 W JP 2012003624W WO 2013179359 A1 WO2013179359 A1 WO 2013179359A1
Authority
WO
WIPO (PCT)
Prior art keywords
moving means
determination
discrimination
data
unit
Prior art date
Application number
PCT/JP2012/003624
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
洋輝 大橋
高行 秋山
Original Assignee
株式会社日立製作所
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to CN201280074463.1A priority Critical patent/CN104412310B/zh
Priority to PCT/JP2012/003624 priority patent/WO2013179359A1/ja
Priority to IN11193DEN2014 priority patent/IN2014DN11193A/en
Priority to JP2014518101A priority patent/JP5816748B2/ja
Publication of WO2013179359A1 publication Critical patent/WO2013179359A1/ja

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

Definitions

  • the present invention relates to a system for determining a moving means of a terminal.
  • Patent Document 1 describes a mobile terminal device that performs support according to a moving means by switching an application program according to the moving means even when a plurality of moving means are used. Specifically, using a direction sensor, temperature sensor, barometric sensor, tilt sensor, gyro sensor, GPS (Global Positioning System) signal receiver, map database, etc. A method for discrimination is described.
  • Non-Patent Document 1 uses a machine learning technique, and based on the movement of a mobile terminal equipped with an acceleration sensor, the movement status of the terminal holder is “stationary”, “walking”, “running”, A method for estimating which of the four states of “bus / train” is described.
  • Patent Document 1 neither of Patent Document 1 and Non-Patent Document 1 takes into consideration the factors of data dispersion within each moving means. Values output by acceleration sensors, gyro sensors, bearing sensors, barometric sensors, tilt sensors, etc. are strongly affected by differences in road conditions, for example, so it is important to consider this in order to perform accurate estimation It is.
  • the moving body for example, a vehicle or a person
  • the moving body has small up / down / left / right / front / back shaking, and thus the shaking transmitted to the terminal is also small.
  • the variation is considered to be small.
  • the moving body is greatly shaken up and down, left and right, and back and forth, so that the vibration transmitted to the terminal is also large. For this reason, it is considered that there is a large variation in the measurement values of various sensors due to this shaking, and it is necessary to set a criterion based on the road conditions.
  • a discrimination method considering the road conditions is required.
  • driver's driving habits can also affect sensor data.
  • a discriminating method considering this factor is also important.
  • the present invention has been made in view of such circumstances, and for example, a method for determining a moving means of a terminal in consideration of factors that affect sensor values such as road conditions, and a system for realizing the method
  • the purpose is to provide.
  • a first sensor a granting unit for giving environmental information about the environment from which the data is acquired to data acquired by the first sensor, and a terminal on which the first sensor is mounted in association with each of the plurality of environmental information
  • a storage unit that stores a discrimination reference value for discriminating the moving means, and a predetermined discrimination reference value is selected from the storage unit according to the given environment information, and the terminal and the terminal using the data and the predetermined discrimination reference value
  • a moving means discriminating system having a moving means discriminating unit for discriminating moving means.
  • 10 is an example of a flowchart for explaining processing of a characteristic moving means detection unit 2011. It is an example of the graph showing the characteristic of the speed at the time of train travel. It is an example of a block diagram of the movement means discrimination
  • a moving means discriminating system 100 that determines a moving means of a terminal holder using an acceleration sensor
  • environmental information related to the environment from which the data was acquired specifically, information related to road conditions such as road attributes and regions is used as an example.
  • driver-specific information such as driving habits and other factors can be handled in the same way as environmental information, and it is also possible to switch the discrimination criteria for multiple factors among them It is.
  • FIG. 1 is an example of a configuration diagram of the moving means discriminating system of the present embodiment.
  • the moving means discriminating system 100 includes an acceleration sensor 101, a moving means discriminating unit 102, a factor-specific discriminant reference database 103, and a factor label assigning unit 104.
  • the acceleration sensor 101 measures data at a predetermined sampling rate.
  • the factor label assigning unit 104 assigns a label relating to the road condition to the measurement value obtained from the acceleration sensor 101.
  • the moving means discriminating unit 102 reads out the discriminant reference value for each road situation stored in the factor-specific discriminant reference database 103, and compares the discriminant reference value with the collected data to move. Determine the means. The determination method will be described later.
  • the moving means discriminating system 100 can be realized by a single terminal having an acceleration sensor such as a smartphone, a calculation unit, and a storage unit.
  • an acceleration sensor such as a smartphone, a calculation unit, and a storage unit.
  • a computer that performs arithmetic processing may be prepared, and the moving means determination unit 102 and the factor-specific determination reference database 103 may be included in the computer.
  • what is necessary is just to implement
  • a portable terminal 200 as shown in FIG. 2 can be used.
  • the central processing unit 203 compares the measurement value obtained from the acceleration sensor 202 with the discrimination reference value for each road situation stored in the discrimination criteria database classified by factor stored in the storage device 204. Then, it is determined whether the moving means is a car or a motorcycle. Data is transmitted / received using the bus 206, for example.
  • the central processing unit 203 reads out and executes various programs recorded in the storage device 204, thereby realizing various functions.
  • the processing performed by the moving means determination unit 102 is realized by the central processing unit 203 reading and executing a moving means determination program recorded in the storage device 204. The same applies to other processes.
  • a computer as shown in FIG. 3 can be used.
  • the measurement value obtained from the acceleration sensor 100 may be transmitted to the computer 300 by connecting the acceleration sensor 100 and the computer 300 using, for example, a USB (Universal Serial Bus) cable or the like, or via a network.
  • the data may be written once on a medium such as a CD or a DVD and then read by the computer 300.
  • the measured values of the acceleration sensor 100 thus obtained in some way are read by the central processing unit 302 to the main storage device 303 and stored in the factor-specific discrimination reference database 103 held in the auxiliary storage device 304.
  • the moving means By comparing with the determination reference value for each situation, it is determined whether the moving means is a car or a motorcycle.
  • Data is transmitted and received using the bus 306, for example.
  • a DRAM Dynamic Random Access Memory
  • SRAM Static Random Access Memory
  • the main storage device 303 As the auxiliary storage device 304, for example, a hard disk, a flash memory, a flexible disk, or the like can be used.
  • the input control device 301 that processes input from the input device 310 such as a mouse or a keyboard
  • the output device 320 such as a display for displaying a discrimination result, and the output are controlled. It is desirable to include an output control device 305 or the like.
  • the central processing unit 302 implements various functions by reading and executing various programs recorded in the auxiliary storage device 304.
  • the processing performed by the moving means determination unit 102 is realized by the central processing unit 302 reading and executing a moving means determination program recorded in the auxiliary storage device 304. The same applies to other processes.
  • the reason for using this is that the acceleration measured in each axis direction depends greatly on the orientation of the terminal, so when you hold it in various ways, such as putting it in a pants pocket, putting it in a breast pocket, or putting it in a bag. , Because it is not possible to obtain a stable value with only the acceleration in each axis direction, but using the norm can handle the magnitude of acceleration regardless of the orientation, so it is considered that a stable value independent of the terminal posture can be obtained. It is.
  • the dispersion value is considered to be a value that well reflects the vibration inherent in the moving means. For example, simply using the absolute value of acceleration to detect the difference between acceleration and deceleration at start and stop, the moving means cannot be determined when driving at a constant speed, but when it is running at a constant speed. By paying attention to the difference in characteristics, it is possible to determine the moving means regardless of whether it is stationary, accelerated / decelerated, or traveling at a constant speed.
  • the reason why the vibration inherent to the moving means occurs will be explained.
  • cars and motorcycles are equipped with a drive system such as an engine, and vibrations due to the drive system occur when the vehicle is operated.
  • the drive system is often installed in the hood at the front of the vehicle.
  • the drive system is often provided just below the seat, and the vibration tends to be transmitted to the passenger.
  • the car is better than the bike because the suspension function that absorbs the vibration caused by the vertical movement of the vehicle, etc., and prevents it from being transmitted to the rider is better than the bike.
  • vibration caused by road surface unevenness or the like is not easily transmitted to the passenger. In this way, there is a certain tendency in how vibration is transmitted depending on the moving means, which is the reason why the vibration inherent in the moving means is generated.
  • the reason for using the median value is to improve the stability of discrimination. For example, when a determination is made based on only one variance value in a short time segment such as 10 seconds (this is called a small segment), acceleration / deceleration was frequently repeated in that segment, especially on a road with poor pavement conditions. An erroneous determination may be made due to various reasons such as accidental large noise in the sensor. On the other hand, for example, 900 seconds, that is, 90 small segments, by using the median value when the dispersion values are collected for a certain period of time (this is called a large segment), the above noise can be prevented. Robust discrimination can be made.
  • the factor label assigning unit 104 assigns a label for each factor that affects the measurement value of the sensor. For example, in the present embodiment, a label relating to road conditions is given. Here, the labels may be given to all the measured values at the same interval as the sensor sampling rate. Alternatively, in order to reduce the amount of data, it is possible to designate a start point and an end point, and assume that all the data between them is the same label, and attach only one label to each set of start point and end point.
  • FIG. 5A shows an example of data with labels.
  • the data is divided into small segments of appropriate length, such as 10 seconds.
  • a norm variance ⁇ is calculated for each small segment.
  • the median value med ⁇ j when the dispersion value ⁇ i is collected for a fixed time (large segment) such as 900 seconds, that is, 90 segments is calculated.
  • the road condition determination reference value corresponding to the label assigned at 402 is read from the factor-specific determination reference database 103 in which a threshold value ⁇ k set in advance for each road condition is stored. For example, a factor label as shown in FIG. 5B and a threshold value for each road attribute (asphalt pavement, asphalt pavement (many unevenness), gravel road,...) Are stored in the judgment standard database.
  • the design may be such that the discrimination reference value is determined for each region as shown in FIG. In any case, it is important to design the database so that the discrimination reference value matches the road condition. Thereby, it is possible to select a discrimination reference value for discriminating between a car and a motorcycle according to the road condition, and the discrimination accuracy can be improved.
  • med ⁇ j and ⁇ k are compared in 407. If med ⁇ j is smaller than the discrimination reference value ⁇ k , the car label is output at 408, and if med ⁇ j is greater than the discrimination reference value ⁇ k , the motorcycle label is output at 409. This is because the above-described inherent vibration is considered to be larger in the motorcycle. This completes the process for one section of the large segment. Actually, this is repeated for the number of large segments, and for each large segment, it is determined whether the moving means is a car or a motorcycle.
  • the label application timing does not necessarily have to be immediately after data reading as described in the present embodiment, and may be applied to each large segment after being divided into large segments, for example. Further, when the terminal and the computer are prepared separately as described above, the terminal can acquire the acceleration data and give it using the GPS information before transmitting it to the computer. In any case, as long as it is possible to determine which determination reference value should be read when determining the moving means of the large segment, the timing of labeling is not limited.
  • an acceleration sensor is used as a sensor for detecting vibrations inherent to the moving means.
  • any gyro sensor, magnetic orientation sensor, or the like can be used as long as the sensor can detect the inherent vibrations. You may implement using another sensor and you may implement using these two or more.
  • the variance value of the norm was calculated at regular time intervals, and an example using the median value when the values were collected for a fixed time period was introduced. Any other index value may be used as long as it can represent a specific vibration, and the average value or quartile may be used instead of the median value.
  • learning data an example of a system that automatically determines a discrimination reference value for discriminating moving means from discrimination criteria determination data collected in advance (hereinafter referred to as learning data) will be described.
  • an appropriate discrimination reference value based on actual data can be determined.
  • an appropriate discrimination reference value can be determined from learning data collected in advance, and the discrimination accuracy can be improved.
  • FIG. 6 is an example of a configuration diagram illustrating a moving function determination system 600 with a learning function according to the second embodiment.
  • the moving means discriminating system 600 with a learning function stores a sensor 606 for collecting learning data and the collected learning data. And a discrimination criterion determining unit 604 that determines a discrimination criterion for each factor affecting the sensor from the data.
  • the moving means discriminating unit 102, the factor-specific judgment criterion database 103, the factor label assigning unit 104, the discrimination criterion determining unit 604, and the discrimination criterion determining database 605 are realized on a computer. To do.
  • the sensor 606 uses the same type of sensor as the sensor 101 used for moving means discrimination.
  • the sensor 606 for collecting learning data the sensor 101 mounted on the terminal where the moving means is to be determined may be used, the acceleration sensor of another terminal may be used, or both of them may be used.
  • a plurality of sensors may be used. In this embodiment, an example using an acceleration sensor will be described as an example.
  • the entire configuration shown in FIG. 6 may be realized on a single terminal using, for example, a smartphone having both sensor and computer functions, or the acceleration sensor 101 and the moving means determination unit.
  • 102, the factor-specific discrimination standard database 103 and the factor label assigning unit 104 are realized by a terminal such as a single smartphone, the discrimination standard determination unit 604, the discrimination standard determination database 605 is a computer, and the acceleration sensor 605 is another smart phone. It may be realized by a terminal.
  • the way of combining hardware is not limited.
  • the moving means discriminating system 100 in FIG. 1 the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • FIG. 7 is an example of data stored in the discrimination criterion determination database 605.
  • a database for determining discrimination criteria in addition to manually labeling the collected data as car data or motorcycle data, for example, taking notes when collecting learning data, etc.
  • a label is provided to indicate what kind of road conditions, such as road attributes and regions, the learning data is. Later, a method for automatically assigning a road condition label by adding another configuration will be described.
  • the median of the variance values described when the processing procedure of FIG. 4 is described in the first embodiment is stored in the discrimination criterion determination database. Since the method of calculating the median of the variance values is the same as that described in the first embodiment, the description thereof is omitted here. Similar to the description in the first embodiment, the norm dispersion value is calculated every fixed time, and in addition to the median value when the values are collected for a fixed time, specific vibrations such as standard deviation and amplitude are calculated. Other index values may be used as long as they can be expressed, or an average value or a quartile may be used instead of the median value.
  • the road status information can be labeled with, for example, road attributes and regions, but in this embodiment, only examples using regions are used. It is described.
  • the method for determining the discrimination reference value is described below.
  • learning data is collected using the acceleration sensor 606 and stored in the discrimination criterion determination database 605.
  • the discrimination criterion determination unit 604 calculates the discrimination criterion value for each road condition with reference to the learning data stored in the discrimination criterion determination database 605 and stores the value in the factor-specific discrimination criterion database 103.
  • SVM Small Vector Vector Machine
  • SVM is a technique for estimating a hyperplane that best separates labeled learning data by solving a convex optimization problem.
  • Equation 1 is an optimization problem having a quadratic objective function and linear constraints, a solution can be obtained without falling into a local optimization problem. Therefore, it may be solved using any existing algorithm such as the steepest descent method or Newton method.
  • the parameter C an appropriate value may be determined while confirming the discrimination accuracy when the value of C is variously changed using learning data by using an intersection confirmation method or the like.
  • ⁇ n w 1 ⁇ n + w 0 is obtained at the time of discrimination.
  • a threshold value that separates the positive and negative values of y ( ⁇ n ), such as ⁇ ⁇ w 0 / w 1 , may be calculated and stored in the factor-specific discrimination reference database 103.
  • a linear discrimination method for example, logistic regression may be used, or a perceptron may be used.
  • logistic regression may be used, or a perceptron may be used.
  • data may be collected via a network.
  • a transmission unit 807 for transmitting data via the network as shown in FIG. 8 may be added to the configuration.
  • the transmission unit 807 may be provided on a computer separately from a terminal on which the acceleration sensor 606 is mounted, and may be realized on the computer, or may be configured on the same terminal as the acceleration sensor 606. Also good.
  • the raw data of the sensor can be transmitted as it is, and the determination of the road condition and the calculation of the median value of the variance value can be performed on the discrimination criterion determination database side.
  • a large amount of data is transmitted via the network, and the load on the network increases.
  • the calculation is performed on the learning data collection terminal side including the sensor 606. It is preferable to use a configuration in which only the calculation result is transmitted.
  • data may be sent in the form of e-mail.
  • uploading may be performed using a data uploading interface as illustrated in FIG. 9.
  • the data acquisition time can be easily determined by distinguishing the car / bike. Any interface may be used as long as transmission is possible.
  • the median value in the large segment can be used to increase the stability of the discrimination, but still when the special driving behavior continues intermittently in the large segment. There is a possibility of misclassification.
  • FIG. 10 is an example of a configuration diagram illustrating the moving function determination system 1000 with a correction function according to the third embodiment.
  • the moving means discriminating system with correction function 1000 includes a time series discriminating information storage unit 1008 in addition to the configuration of the moving means discriminating system 100 described in the first embodiment.
  • the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • a moving unit determination unit 102, a factor-specific determination reference database 103, a factor label assignment unit 104, and a time-series determination information storage unit 1008 are realized on a computer.
  • the entire configuration illustrated in FIG. 10 may be realized on a single terminal using, for example, a smartphone having both sensor and computer functions. In any case, as long as the function shown in FIG. 10 can be realized, the way of combining hardware is not limited.
  • FIG. 11 is an example of a flowchart illustrating a procedure in which the moving means determination unit 102 performs moving means determination using the time series determination information storage unit 1008.
  • moving means discrimination is performed by the method described in the first or second embodiment.
  • the temporary discrimination result obtained at 1101 is stored in the time series discrimination information storage unit 1008.
  • the information is used to correct the discrimination result, and the label of the car or motorcycle is output and the process ends.
  • FIG. 12 is an example of data stored in the time-series discrimination information storage unit 1008.
  • the determination is made for each large segment, and is stored in the time series determination information storage unit 1008 together with the time information.
  • the determination reference value is obtained by learning, and when the determination reference value is determined, the likelihood of the determination result is output.
  • the likelihood is equal to or less than a certain value, Only the correction process may be performed, and the correction method does not matter.
  • the characteristics of vibration transmitted to the terminal of the person riding on the vehicle are different between when the vehicle is stationary and when it is running.
  • the difference can be absorbed to some extent by using the variance value, but the discrimination accuracy is further improved by using different discrimination reference values at the time of stationary and running. Can do.
  • the vibration transmitted to the terminal is larger when running than when stationary.
  • the conditions such as the terminal, vehicle, how to hold or install the terminal, road conditions, etc. are the same, the difference between the magnitude of vibration when stationary and the magnitude of vibration when traveling is almost constant. . Therefore, if a section with a small vibration and a section with a large vibration are separated in continuous data, it can be determined that each corresponds to a stationary state and a traveling state.
  • FIG. 13 is an example of a configuration diagram of a moving means discriminating system with a stationary travel judging function in the fourth embodiment.
  • the moving means determination system 1300 with a stationary travel determination function includes a stationary travel determination unit 1309.
  • the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • the moving means determination unit 102, the factor-specific determination reference database 103, the factor label assignment unit 104, and the stationary travel determination unit 1309 are realized on a computer.
  • the entire configuration shown in FIG. 13 may be realized on a single terminal using, for example, a smartphone having both sensor and computer functions. In any case, as long as the function shown in FIG. 13 can be realized, the way of combining hardware is not limited.
  • L i represents the set of data attached labels i
  • N i denotes the number of data attached label i.
  • the class with the larger value may be judged as “running” and the class with the smaller value as “still”.
  • the method is not limited to the k-means method, and a method such as hierarchical clustering or a self-organizing map is used. May be used instead.
  • the stationary travel determination is performed before data storage, and the data is stored with the label of stationary or traveling.
  • the determination reference value is determined by learning using the configuration as described in the second embodiment
  • the learning data is also described in the present embodiment when the determination reference determination database 605 is constructed.
  • FIG. 15A shows an example of data held by the factor-specific discrimination reference database 103 in the present embodiment.
  • FIG. 15B shows an example of data held in the discrimination criterion determination database 605 when constructing a moving means discrimination system with a learning function as described in the second embodiment.
  • the database described in the embodiments so far is provided with other information on stationary travel.
  • the position information of the terminal can be obtained from the GPS information, it is possible to calculate the moving speed of the vehicle by using it, thereby making it possible to determine whether the vehicle is stationary or running.
  • a method of performing stationary running determination from GPS information when a GPS receiver is added to the configuration will be described using FIG.
  • GPS information is read. From the GPS information, the position information of the terminal and the time information acquisition information can be obtained. Assuming that the (latitude, longitude) in decimal notation received at times t 1 and t 2 is (lat 1 , lon 1 ) (lat 2 , lon 2 ), the speed between them is 1602 ( It can be calculated as shown in Equation 3).
  • r in (Expression 3) is a value representing the radius of the earth.
  • the average value meanv j of the speed is calculated for each large segment. Then, in 1604, the average speed meanv j is compared with a threshold value ⁇ for determination of stationary travel. If meanv j is equal to or smaller than ⁇ , it is determined that the vehicle is stationary in 1605, and if meanv j is larger than ⁇ , it is determined that the vehicle travels in 1606. To do.
  • a threshold value ⁇ for example, a value such as 5 km / h can be used.
  • the speed may be calculated by integrating the acceleration, and for example, the stationary / running determination may be performed from the speed information by the method described in the present embodiment.
  • the discrimination means described in the above-described embodiments is applied to the section in which walking is detected and the portion is excluded, so that the moving means for data including walking can be discriminated. It can be performed with high accuracy. For this reason, for example, when generating traffic information after making a moving means determination, by excluding walking, it is erroneously recognized as a vehicle traveling on a road such as a car or a motorcycle. Traffic information can be generated with high accuracy.
  • FIG. 17 is an example of a configuration diagram illustrating a moving means determination system 1700 with a walking exclusion function in the fifth embodiment.
  • the moving means determination system 1700 with a walking exclusion function includes a walking detection unit 1710.
  • the moving means discriminating system 100 in FIG. 1 the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • the moving unit determination unit 102, the factor-specific determination reference database 103, the factor label assignment unit 104, and the walking detection unit 1710 are realized on a computer.
  • the entire configuration shown in FIG. 17 may be realized on a single terminal using, for example, a smartphone having both sensor and computer functions. In any case, as long as the function shown in FIG. 17 can be realized, the way of combining hardware is not limited.
  • FIG. 18A and FIG. 18B are examples of walking data when the horizontal axis represents time and the vertical axis represents the norm value of the acceleration sensor.
  • One scale on the horizontal axis corresponds to 2 seconds.
  • FIG. 18A and FIG. 18B it can be seen that there are peaks in the sensor data about twice a second. This can be considered as a feature that appears because a person walks about two steps per second. It can be considered that the peaks of different sizes appearing alternately in FIG. 18 (a) are the difference between when the right foot is stepped on and when the left foot is stepped on. Depending on how the terminal is held, the size of the mountain may differ depending on whether the left or right foot is stepped on, or may be almost the same as shown in FIG. 18B. In any case, as a cycle, a mountain appears about twice a second.
  • FIG. 18 (c) is obtained by subjecting the acceleration data of FIG. 18 (a) to Fourier transform
  • FIG. 18 (d) is obtained by subjecting the acceleration data of FIG. 18 (b) to Fourier transform.
  • the horizontal axis represents frequency (unit: Hz)
  • the vertical axis represents power. It can be seen from FIGS. 18C and 18D that a strong peak appears in the frequency band of about 2 Hz. This can be interpreted as a result reflecting the walking of about two steps per second as described above. That is, it is possible to detect walking by capturing this feature in the frequency domain.
  • the obtained sensor data is divided into appropriate time intervals (for example, about 10 seconds).
  • Fourier transform is applied to the data in the section.
  • the moving means when the moving means is determined, if the data containing various moving means is determined using only the same reference, the determination accuracy may be lowered.
  • some moving means have characteristics unique to the moving means, and it is considered that the moving means can be detected by capturing the features.
  • such a moving means is detected, and the discrimination means described in the above-described embodiments is applied to the section excluding that part, thereby accurately determining the moving means.
  • the discrimination means described in the above-described embodiments is applied to the section excluding that part, thereby accurately determining the moving means.
  • it will accidentally run on roads such as cars and motorcycles. It is possible to prevent erroneous recognition as a vehicle and to generate accurate traffic information.
  • FIG. 20 is an example of a configuration diagram showing a moving means discrimination system 2000 with a characteristic moving means excluding function.
  • the moving means discrimination system 2000 with the characteristic moving means excluding function includes a characteristic moving means detector 2011 and a GPS receiver 2012 in addition to the configuration of the moving means discrimination system 100 described in the first embodiment.
  • the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • a moving means discriminating section 102, a factor-specific judgment reference database 103, a factor label assigning section 104, and a characteristic moving means detecting section 2011 are realized on a computer, and the acceleration sensor 101
  • the GPS receiver 2012 is realized by another terminal. In addition to this configuration, the entire configuration shown in FIG.
  • the 20 may be realized on one terminal by using, for example, a smartphone having both sensor and computer functions. For example, only the GPS receiver 2012 may be used. You may implement
  • characteristic moving means detection unit 2011 trains, bicycles, airplanes, and ships are described as examples of characteristic moving means.
  • a method for detecting an airplane that is considered relatively easy to detect will be described.
  • One of the characteristics of an airplane when compared with other vehicles is that it has a high moving speed. For example, there are few examples of vehicles other than airplanes that exceed 500 km / h. Therefore, a speed threshold value is set, and when a speed faster than this is detected in 2101, the moving means in that section is determined to be an airplane, an airplane label is output, and the process is terminated. Since the speed can be calculated using GPS information as described in the fourth embodiment, the description thereof is omitted.
  • a characteristic of a ship when compared to other vehicles is that it moves over the sea or lake. Airplanes may also move between these locations, but since plane data is excluded in 2101, it may be considered here that only ships move in these locations. Since position information is obtained from the GPS receiver 2012, when it is detected in 2102 that these places are moving, it is determined that the moving means in the section is a ship, and the processing is terminated.
  • a characteristic of a bicycle when compared with other vehicles is that a periodic rhythm appears in the data. Such a rhythm is also detected during walking, but the walking section can be detected by the method described in Example 5 separately, so if walking is detected first, there is no need to consider it.
  • frequency conversion is performed to detect the periodic rhythm.
  • a strong peak at a certain threshold value theta f following frequency bands appeared at 2103, to determine the moving means of the section with a bicycle The process is terminated.
  • the value of the threshold value ⁇ f can be determined by a method of learning from separately collected bicycle data, for example.
  • t 1 , t 5 , t 9 are sections stopping at the station
  • t 2 , t 6 are sections leaving the station and accelerating
  • t 3 , t 7 are constant speeds between the stations.
  • the running sections, t 4 and t 8 are sections decelerating before arrival at the station.
  • the position and speed can be calculated by the method described in the previous examples. If such a feature can be detected in 2104, it is determined that the moving means in that section is a train, and the process is terminated.
  • train, bicycles, airplanes, and ships are described as examples of characteristic moving means, but other moving means can be similarly detected by capturing characteristics unique to the moving means.
  • FIG. 23 is an example of a configuration diagram illustrating a moving means determination system 2300 with a multi-sensor integration function in the seventh embodiment.
  • the movement means determination system 2300 with a multi-sensor integration function includes other sensors 2313 and an integrated movement means determination section 2315. Instead, a sensor-specific moving means determination unit 2314 is provided.
  • the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • a factor-specific judgment reference database 103, a factor label assigning unit 104, a sensor-specific moving unit discriminating unit 2314, and an integrated moving unit discriminating unit 2315 are realized on a computer.
  • the sensor 101 and the other sensors 2313 are realized by different terminals.
  • the entire configuration shown in FIG. 20 may be realized on a single terminal using, for example, a smartphone having both sensor and computer functions. It may be realized by a terminal. In any case, as long as the function shown in FIG. 23 can be realized, the way of combining hardware is not limited.
  • the moving means discriminating method described in the above embodiments is applied to each sensor, and moving means discrimination for each sensor is performed. That is, first, the data is divided into small segments, the norm variance value is calculated, and then the median value of a large segment obtained by collecting a certain number of them is calculated. Then, for example, the criterion value for each factor such as the road condition is read from the factor-specific criterion database 103 and compared with it to determine the moving means for the segment of interest.
  • the discrimination reference value is provided for each sensor as shown in FIG.
  • the discrimination reference value for each sensor may be determined using learning data by the method described in the second embodiment, for example. In that case, after learning data is collected for each sensor, the discrimination reference value is learned.
  • the discrimination criterion determination database is as shown in FIG.
  • walking detection, stationary running determination, automatic determination of road conditions, correction of determination results using time-series determination results, and the like are performed. It's okay.
  • the integrated moving means discriminating unit 2315 uses these results in an integrated manner and outputs a final moving means discrimination result.
  • Equation 4 when discriminating between a car and a motorcycle, it can be discriminated that the vehicle is discriminated when the inequality represented by (Equation 4) is established, and that the vehicle is discriminated when it is not.
  • s i in (Equation 4) is the type of sensor
  • S is a set of sensors
  • w i is a weight to be given to the sensor s i
  • C i is 1 when the determination result of the sensor s i is a car
  • B i is 0 when the determination result of the sensor s i was a car, is a value of 1 when was motorcycle.
  • Highly accurate discrimination can be performed by appropriately setting the weight w i given to each sensor. It is possible to appropriately set a weight by detecting a time section in which noise is larger than that of other sensors and reducing the weight of the sensor for the time section. For example, if the acceleration / deceleration is intermittently continued, the acceleration sensor is reduced in weight. Further, the weight of the gyro sensor is reduced in a section with many curves. Alternatively, the weight of the magnetic azimuth sensor is reduced in a section traveling near a place where electromagnetic disturbance is large.
  • the integration method described in the present embodiment is an example, and weighting can be performed by defining the reliability for each sensor in advance.
  • weighting can be performed by defining the reliability for each sensor in advance.
  • the integration method such as assigning reliability to the discrimination result based on the degree of deviation from the discrimination reference value and using the value as a weight when discriminating each sensor.
  • the accuracy of the determination may be reduced due to noise caused by some factor, such as noise on the sensor value when the terminal has heat. Can happen.
  • the information of the terminals existing in the vicinity can be used, stable discrimination can be performed even in the presence of such noise. For example, if there are 5 or more terminals within a few meters, they take almost the same movement trajectory, and the characteristics of sensor data are similar, they move on the same vehicle. Can be determined. Assuming that only one of these terminals outputs a different discrimination result, the discrimination result can be corrected so as to be consistent with other terminal information.
  • the configuration of the present embodiment it is possible to determine the moving means with high accuracy by performing correction processing for moving means determination on the server side using information of surrounding terminals.
  • FIG. 25 is an example of a configuration diagram illustrating a moving means determination system with surrounding terminal information utilization function in the eighth embodiment.
  • the moving means determination system 2500 with surrounding terminal information utilization function includes a GPS receiver 2516, a moving means determination result database 2517, and a determination result correction unit 2518. ing.
  • the moving means discriminating system 100 in FIG. 1 the description of the components having the same functions as those already described with reference to FIG. 1 is omitted.
  • the hardware configuration for example, as shown in FIG. 25, the moving means discriminating unit 102, the factor-specific judgment reference database 103, and the factor label assigning unit 104 are realized on a computer, the moving means discriminating result database 2517, the discrimination result correcting unit. 2518 is realized on a server, and the acceleration sensor 101 and the GPS receiver 2516 are realized on different terminals.
  • a smartphone having both sensor and computer functions may be used, so that parts other than the server may be realized on one terminal, and the functions of the computer and server may be realized by one computer. It may be realized. In any case, as long as the function shown in FIG. 25 can be realized, the way of combining hardware is not limited.
  • the moving means determination is performed by the method described in the embodiments so far, and the result is stored in the moving means determination result database 2517.
  • the ID of the terminal, the latitude / longitude information of the terminal obtained by using the GPS receiver 2516, and the date / time when the GPS information was received are also stored together for use in searching for surrounding terminals.
  • FIG. 26 shows an example of data stored in the moving means determination result database.
  • the discrimination result correction unit 2518 searches for peripheral terminals of a certain terminal based on the data stored in the moving means discrimination result database 2517. Specifically, for example, a terminal ID whose position and time are within a certain value is searched from the moving means determination result database 2517. Assuming that (latitude, longitude) in decimal notation is (lat 1 , lon 1 ) (lat 2 , lon 2 ), the distance d between these two points can be calculated by (Equation 5). Here, r in (Expression 5) is a value representing the radius of the earth.
  • the time difference can be calculated as
  • ⁇ t are satisfied by using these and appropriate threshold values ⁇ d and ⁇ t , it is assumed that the two terminals transmitting these data exist in the vicinity. judge.
  • ⁇ d and ⁇ t for example, 10 m, 1 second, etc. can be used. When such a condition continues for a certain time or longer, for example, continuously for 10 minutes or longer, it can be determined that these terminals exist on the same vehicle.
  • the moving means discrimination result is corrected.
  • the discrimination between a car and a motorcycle when there are N terminals that are determined to exist on the same vehicle, if the determination result at half of the terminals exceeding N / 2 is a car, the same vehicle If the discrimination result of all terminals judged to be on the vehicle is a car, and the discrimination result on a terminal exceeding N / 2 is a motorcycle, the discrimination result of all terminals judged to be on the same vehicle is a motorcycle, If the result is divided into the same number of N / 2, it is sufficient not to perform correction.
  • the method of correction is not limited to this, and the determination result is based on the degree of deviation from the determination reference value when determining the reliability for each terminal in which the reliability is pre-defined and weighted.
  • Various correction methods are possible, for example, by assigning a reliability to and using the value as a weight.
  • FIG. 27 is an example of a configuration diagram illustrating a moving means determination system 2700 with an automatic road condition determination function in the ninth embodiment.
  • the moving means discriminating system 2700 with road condition automatic determination function includes GPS receivers 2719 and 2721 and road condition determining units 2720 and 2722 in addition to the configuration of the moving means discriminating system 600 with learning function described in the second embodiment. Yes. Note that the processing of the factor label assigning unit 104 is realized by the road condition determining units 2720 and 2722. In the moving means discrimination system 600 with learning function in FIG. 6, the description of the components having the same functions as those already described with reference to FIG. 6 is omitted.
  • the moving means discriminating unit 102, the factor-specific judgment criterion database 103, the discrimination criterion determining unit 604, and the discrimination criterion determining database 605 are realized on a computer, and the acceleration sensor 101,
  • the GPS receiver 2719 and the road condition automatic determination unit 2720 are realized by a terminal such as a smartphone (referred to as a discrimination terminal), and the acceleration sensor 606, the GPS receiver 2721, and the road condition automatic determination unit 2722 are terminals such as a smartphone ( This is called a collection terminal).
  • the discrimination terminal and the collection terminal may be realized by the same terminal, or the discrimination terminal and the computer part are used by using a smartphone having both the sensor and the computer functions. May be realized on one terminal, or all the configurations shown in FIG. 27 may be realized on one terminal. In any case, as long as the function shown in FIG. 27 can be realized, the way of combining hardware is not limited.
  • a procedure for the road condition determination unit to generate a road condition label from the GPS information will be described.
  • a region ID that is uniquely determined is generated using latitude and longitude information, and is used as a road status label when this is stored in the factor-specific discrimination criterion database 103 or the discrimination criterion determination database 605.
  • the GPS information includes the latitude / longitude information of the GPS receiver.
  • these latitudes and longitudes are expressed in decimal notation.
  • a region ID that is uniquely determined can be generated by dividing each of the regions by 0.1 degrees and giving a unique ID to each region.
  • latitude 0.1 degree corresponds to about 11km
  • longitude 0.1 degree is less than that (about 11km on the equator, the distance decreases with increasing latitude), so this is about 11km square or less
  • Corresponds to the area in such a region, it can be used as an ID indicating the road condition because it is unlikely that the road condition will greatly change in the region except for some exceptions.
  • the generated ID is A 356,1397 .
  • the factor-specific discrimination standard database 103 and the discrimination standard determination database 605 are constructed. Specifically, the area ID may be used in place of the road attributes in FIG. 5B and the areas in FIGS. 5C and 7. It is also possible to search for a region or road attribute corresponding to the region ID using map information or the like and use them as a label representing the road condition.
  • FIG. 28A shows an example of a factor-specific discrimination standard database in this embodiment
  • FIG. 28B shows an example of a discrimination standard determination database.
  • a database for road condition determination is separately prepared, and information on latitude and longitude is previously stored therein.
  • the area ID may be stored, and the area ID (that is, the average road condition of the area) may be obtained by referring to the information.
  • the coordinates of the representative point of the area may be determined, and the area ID may be given if the distance from the representative point is within a certain value.
  • an unsupervised learning method such as the k-means method may be applied to divide the learning data into k classes based on latitude and longitude, and k road status IDs may be generated accordingly.
  • a method such as a k-nearest neighbor method may be used, and means for determining a road condition label from GPS data is not limited.
  • the database for storing map information can be used to distinguish roads such as “highways”, “general roads” and “farm roads” by using existing map matching techniques. You may make it use as a label to represent.
  • the label may be estimated with high accuracy from time series information. Therefore, if such data is used effectively, a large amount of data can be collected without taking time and effort for labeling, and a discrimination reference value based on the data can be set.
  • a small amount of data with a label of a moving means and a large amount of data without a label of a moving means are used, and data without a label is also used as learning data.
  • data without a label is also used as learning data.
  • FIG. 29 is an example of a configuration diagram illustrating a moving means determination system 2900 with an unlabeled data learning function in the tenth embodiment.
  • An unlabeled data learning function-equipped moving means discrimination system 2900 includes a labelless discrimination standard correction database 2925 and a discrimination standard correction unit 2926 in addition to the configuration of the learning function-equipped moving means discrimination system 600 described in the second embodiment. .
  • the moving means determination system 600 with a learning function in FIG. 6 the description of the components having the same functions as those already described in FIG. 6 is omitted.
  • the hardware configuration includes a moving means determination unit 102, a factor-specific determination criterion database 103, a factor label assignment unit 104, a determination criterion determination unit 604, a determination criterion determination database 605, and an unlabeled determination criterion.
  • the correction database 2925 and the discrimination reference correction unit 2926 are realized on a computer, and the acceleration sensor 101 and the acceleration sensor 606 are realized on different terminals. In addition to this configuration, for example, the same acceleration sensor 101 and acceleration sensor 606 may be used, or the entire configuration shown in FIG. You may implement
  • the learning data with the label of car or motorcycle is read from the discrimination standard determination database 605, and the discrimination standard value is determined according to the method described in the second embodiment.
  • the discrimination reference correction unit 2926 reads data that is not labeled as a car or a motorcycle from the unlabeled discrimination reference correction database 2925, and applies the moving means discrimination method described in the second embodiment.
  • the unlabeled discrimination reference correction database 2925 stores data that is not labeled as a car or a motorcycle as shown in FIG.
  • a result as shown in FIG. 31B is obtained.
  • the determination result of t i-1 is likely to be a car. Therefore, next, at 3003, the number Nj of data determined to require such correction is counted for each road condition.
  • whether or not correction is necessary is determined when, for example, the determination result differs only in the data of interest among the total of 5 data of the data of interest and the two data before and after the data of interest. It may be determined that correction is necessary.
  • the GPS information is used to determine the road situation. Therefore, the data when the GPS data cannot be acquired is used as learning data. Cannot be used. Alternatively, even if it can be obtained, it is not appropriate to use it if the accuracy is extremely poor. Positioning using GPS information is impossible in principle unless radio waves can be received from four or more GPS satellites. For example, when it is hidden in a tall building or when traveling in a tunnel, it often happens that radio waves cannot be received from four or more GPS satellites and the accuracy is lowered.
  • the GPS signal when the GPS signal is not received or the accuracy is remarkably low even if the GPS signal is received, the data on the road condition is manually labeled as described in the first embodiment. If it is not transmitted, it cannot be used as learning data. For this reason, even if data without a label is transmitted, uselessness occurs.
  • FIG. 32 is an example of a configuration diagram illustrating a moving means determination system 3200 with a data transmission control function according to the eleventh embodiment.
  • the moving means discriminating system with data transmission control function 3200 has a transmission unit 807 described in the second embodiment and a transmission having a new configuration in addition to the configuration of the moving means discriminating system with road condition determining function 2700 described in the ninth embodiment.
  • a control unit 3227 is provided.
  • the transmission control unit 3227 controls whether or not to transmit data depending on whether or not the GPS data can be acquired and whether or not the GPS data is sufficiently accurate.
  • description is abbreviate
  • the moving means discriminating unit 102, the factor-specific judgment criterion database 103, the discrimination criterion determining unit 604, and the discrimination criterion determining database 605 are realized on a computer, and the acceleration sensor 101,
  • the GPS receiver 2719 and the road condition automatic determination unit 2720 are realized by a terminal such as a smartphone (referred to as a determination terminal), and include an acceleration sensor 606, a GPS receiver 2721, a road condition automatic determination unit 2722, a transmission control unit 3227,
  • the transmission unit 807 is realized by a terminal such as a smartphone (referred to as a collection terminal).
  • the discrimination terminal and the collection terminal may be realized by the same terminal, or the discrimination terminal and the computer part are used by using a smartphone having both the sensor and the computer functions. May be realized on a single terminal, or all the configurations shown in FIG. 32 may be realized on a single terminal. In any case, as long as the function shown in FIG. 32 can be realized, the way of combining hardware is not limited.
  • the moving means discriminating system with a road condition determination function described in the ninth embodiment when the GPS signal cannot be received or is received, the road condition is extremely low. Cannot be determined. Therefore, even if only the learning data collection sensor can be measured, it cannot be used as learning data, and even if the data is transmitted, waste is generated. Therefore, it can be said that it is not necessary to measure the acceleration sensor in the first place when the GPS information is not acquired or when the accuracy is remarkably low.
  • the power consumption and the memory consumption can be reduced by performing the measurement by the acceleration sensor only when the GPS information can be acquired and the accuracy is sufficient.
  • FIG. 33 is an example of a configuration diagram illustrating a moving means determination system 3300 with a sensor On / Off switching function according to the twelfth embodiment.
  • the moving means discriminating system 3300 with the sensor On / Off switching function includes a measurement On / Off switching unit 3328 in addition to the configuration of the moving means discriminating system 2700 with the road condition determining function described in the ninth embodiment.
  • the measurement On / Off switching unit 3328 controls whether the acceleration sensor is turned on or turned off depending on whether GPS data is acquired and whether the accuracy is sufficient.
  • description is abbreviate
  • the moving means discriminating unit 102, the factor-specific judgment criterion database 103, the discrimination criterion determining unit 604, and the discrimination criterion determining database 605 are realized on a computer, and the acceleration sensor 101,
  • the GPS receiver 2719 and the road condition automatic determination unit 2720 are realized by a terminal such as a smartphone (referred to as a determination terminal), and the acceleration sensor 606, the GPS receiver 2721, the road condition automatic determination unit 2722, and the measurement on / off switching.
  • the unit 606 is realized by a terminal such as a smartphone (referred to as a collection terminal).
  • the discrimination terminal and the collection terminal may be realized by the same terminal, or the discrimination terminal and the computer part are used by using a smartphone having both the sensor and the computer functions. May be realized on one terminal, or all the configurations shown in FIG. 33 may be realized on one terminal. In any case, as long as the function shown in FIG. 33 can be realized, the way of combining hardware is not limited.
  • FIG. 34 is an example of a configuration diagram illustrating a road congestion situation estimation system 3400 according to the thirteenth embodiment.
  • the road congestion situation estimation system 3400 includes the moving means determination system 100 described in the first embodiment, a congestion situation estimation database 3401, a congestion situation estimation unit 3402, and a GPS receiver 3403.
  • the set of the moving means discriminating system 100 and the GPS receiver is not limited to one, and a plurality of moving means discriminating systems and GPS receivers may be used. Further, the moving means discriminating system 100 may be replaced by using a moving means discriminating system provided with other functions described in the second embodiment or later.
  • a congestion state estimation database 3401 and a congestion state estimation unit 3402 are realized by a single computer.
  • the configuration of the moving unit determination system 100 may be realized by separately preparing a sensor and a computer, or may be realized on one terminal using a smartphone or the like.
  • the congestion state estimation database 3401 and the congestion state estimation unit 3402 may be realized by using a computer used for moving means determination. In any case, as long as the function shown in FIG. 34 can be realized, the way of combining hardware is not limited.
  • each moving means determination system 100 performs moving means determination.
  • the terminal ID, the position information (latitude and longitude) obtained from the GPS receiver 3403, the speed that can be calculated by the method described in the fourth embodiment from the GPS information, and the data acquisition date and time are transmitted together.
  • the data shown in FIG. 35 is stored in the congestion state estimation database 3401, for example.
  • the length of a large segment, which is a unit for determining the moving means is 900 seconds, for example, and data for estimating road congestion status is generated every second, 900 lines of data shown in FIG. Are sent together.
  • the determination results of the moving means are the same for the 900 rows of data.
  • the congestion state estimation unit 3402 estimates the road congestion state. Specifically, for example, the region ID is determined from the latitude and longitude information by the method described in the ninth embodiment, and the average speed is calculated for each car and motorcycle within the region. If the value is below a certain value, it is congested, and if it is above a certain value, it is judged that the vehicle is not congested separately. Thereby, the congestion situation of the car and motorcycle of an applicable area can be estimated. Of course, in addition to these two stages, it can be divided into multiple stages according to the average speed, such as vacant, somewhat vacant, normal, somewhat congested, and congested.
  • the average speed may be converted by some function to calculate a continuous road congestion index.
  • the road congestion index I can be calculated as (Equation 7).
  • v Max is the maximum speed in the area or road such as legal speed
  • v Min is the minimum speed such as 0
  • v mean is the average speed calculated as described above.
  • the road congestion condition is applied by applying these existing technologies. May be estimated.
  • any means for estimation may be used as long as the congestion state estimation data is collected and the congestion state with respect to each of the car and the motorcycle can be estimated after the moving means is determined.
  • Example 13 describes an example of a road congestion situation estimation system that does not require road map information. By dividing the area that can be limited from GPS information, it is possible to estimate the road congestion in a certain area. However, in order to estimate more detailed information, for example, the congestion situation for each road and link it to an actual road, a road map is required. Therefore, in this embodiment, a system that can estimate a detailed road congestion state associated with an actual road by using a map database will be described.
  • FIG. 36 is an example of a configuration diagram illustrating the map use road congestion situation estimation system in the fourteenth embodiment.
  • the map use road congestion situation estimation system 3600 includes a map database 3605 and a map utilization congestion situation estimation unit 3604 instead of the congestion situation estimation unit 3402. Yes.
  • the description of the components having the same functions as those already described with reference to FIG. 34 is omitted.
  • a congestion state estimation database 3401, a map use congestion state estimation unit 3604, and a map database 3605 are realized by a single computer.
  • the configuration of the moving unit determination system 100 may be realized by separately preparing a sensor and a computer, or may be realized on one terminal using a smartphone or the like.
  • the congestion state estimation database 3401 and the congestion state estimation unit 3402 may be realized by using the computer used for moving means determination, and the map utilization database is realized on another computer. You may do it.
  • the way of combining hardware is not limited.
  • the map information utilization congestion state estimation unit 3604 reads map information from the map database 3605 and obtains the road on which the moving means determination system 100 existed at the time of data transmission by an arbitrary existing method such as map matching.
  • map matching if it can be determined which road is running, as described in the thirteenth embodiment, the speeds of the cars and motorcycles are totaled for each road, and the average value of the speeds is calculated. If the value is below a certain value, the road is congested, and if it is above a certain value, it is determined that the road is not congested separately for each car and motorcycle. Thereby, the congestion situation of the car and motorcycle of an applicable area can be estimated.
  • the road congestion state is displayed by overlapping with a map for each moving means, or the display method is changed for each moving means, the visibility is improved, for example, for each moving means in a certain area.
  • the traffic flow can be grasped intuitively.
  • this road congestion information for each means of transportation for example, when making a new road construction plan or setting new traffic regulations, for example, the number of motorcycle lanes, car lanes, and bus lanes should be set appropriately. It is possible to make various plans according to the local circumstances, such as by restricting traffic on certain roads so that only buses can pass during rush hour.
  • FIG. 37 is an example of a configuration diagram illustrating a road congestion state estimation system with a congestion state display function by moving means according to the fifteenth embodiment.
  • the road congestion state estimation system 3700 with a congestion state display function by moving means includes a congestion state display unit 3706 by movement means in addition to the configuration of the road congestion state estimation system 3400 described in the thirteenth embodiment.
  • the description of the components having the same functions as those already described with reference to FIG. 34 is omitted.
  • a congestion state estimation database 3401, a congestion state estimation unit 3402, and a road state display unit 3706 for each moving means are realized by a single computer.
  • the configuration of the moving unit determination system 100 may be realized by separately preparing a sensor and a computer, or may be realized on one terminal using a smartphone or the like.
  • the congestion state estimation database 3401 and the congestion state estimation unit 3402 may be realized by using the computer used for the movement unit determination, and the movement state-specific congestion state display unit 3706 is different. It may be realized on a computer. In any case, as long as the function shown in FIG. 37 can be realized, the way of combining hardware is not limited.
  • the operation of the congestion status display unit 3706 for each moving means will be described.
  • a method of displaying the congestion status according to the map using the map database 3605 described in the embodiment 14 will be described. Thereby, it is possible to grasp a more detailed road congestion situation.
  • map information is not available, the same method can be realized if the method described in the present embodiment, which is applied to each road, is obtained for each region ID using the method described in the thirteenth embodiment and applied to each region ID. .
  • the congestion status display unit 3706 for each moving means receives the congestion status estimation result for each moving means for each road from the congestion status estimation unit 3402. According to the result, for example, as shown in FIG. 38, the color is classified for each moving means, and the average speed is represented by the length of the arrow, and the road congestion state is displayed on the display device.
  • the average speed may be represented by shades of color, or an animation function may be added to display an icon representing a moving means or a color-coded mark for each moving means.
  • the vehicle may be moved on the road, and the speed of the movement may be set according to the average speed calculated by the road congestion state estimation unit.
  • the visibility can be further improved by adding a device such as highlighting a busy intersection.
  • a device such as highlighting a busy intersection.
  • road congestion will vary from time to time. Therefore, for example, the above-described display can be divided and displayed every hour to make it easy to grasp the transition of the traffic volume for each time zone.
  • adding a storage unit as necessary and accumulating such information, for example, when a certain traffic regulation is performed, how the traffic flow is changed by the current traffic It can also be grasped by comparing the road congestion situation and the past road congestion situation.
  • any means may be used as long as it is a method for improving the visibility at the time of display by changing the display method according to the moving means or changing the display method such as color coding or shape.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Arrangements For Transmission Of Measured Signals (AREA)
  • Navigation (AREA)
PCT/JP2012/003624 2012-06-01 2012-06-01 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム WO2013179359A1 (ja)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CN201280074463.1A CN104412310B (zh) 2012-06-01 2012-06-01 移动方式判别系统以及移动方式判别装置
PCT/JP2012/003624 WO2013179359A1 (ja) 2012-06-01 2012-06-01 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム
IN11193DEN2014 IN2014DN11193A (ko) 2012-06-01 2012-06-01
JP2014518101A JP5816748B2 (ja) 2012-06-01 2012-06-01 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2012/003624 WO2013179359A1 (ja) 2012-06-01 2012-06-01 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム

Publications (1)

Publication Number Publication Date
WO2013179359A1 true WO2013179359A1 (ja) 2013-12-05

Family

ID=49672615

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2012/003624 WO2013179359A1 (ja) 2012-06-01 2012-06-01 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム

Country Status (4)

Country Link
JP (1) JP5816748B2 (ko)
CN (1) CN104412310B (ko)
IN (1) IN2014DN11193A (ko)
WO (1) WO2013179359A1 (ko)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016018308A (ja) * 2014-07-07 2016-02-01 日本電信電話株式会社 移動手段推定装置、その動作方法およびプログラム
JP2018059884A (ja) * 2016-10-07 2018-04-12 日本無線株式会社 物標識別装置、プログラム及び方法
JP2018536223A (ja) * 2015-10-15 2018-12-06 インタートラスト テクノロジーズ コーポレイション センサ情報管理システム及び方法
JP2019139462A (ja) * 2018-02-09 2019-08-22 株式会社Ｓｃｒｅｅｎホールディングス 混雑状況推定方法、混雑状況推定プログラム、および混雑状況推定システム
JP2020520520A (ja) * 2017-05-16 2020-07-09 ケンブリッジ モバイル テレマティクス，インク．Ｃａｍｂｒｉｄｇｅ Ｍｏｂｉｌｅ Ｔｅｌｅｍａｔｉｃｓ， Ｉｎｃ． トリップの種類を識別するためのテレマティクスデータの使用
US11030655B2 (en) 2018-11-02 2021-06-08 International Business Machines Corporation Presenting targeted content to vehicle occupants on electronic billboards
WO2021181469A1 (ja) 2020-03-09 2021-09-16 富士通株式会社 処理選択プログラム、処理選択システム、および処理選択方法
US11713967B2 (en) * 2017-10-13 2023-08-01 JVC Kenwood Corporation Angular speed derivation device and angular speed derivation method for deriving angular speed based on output value of triaxial gyro sensor
JP7395062B1 (ja) 2022-04-28 2023-12-08 楽天グループ株式会社 情報処理装置、情報処理方法、およびプログラム
JP7401716B1 (ja) 2022-04-28 2023-12-19 楽天グループ株式会社 情報処理装置、情報処理方法、プログラム、および学習モデル
JP7430835B1 (ja) 2023-04-19 2024-02-13 パシフィックコンサルタンツ株式会社 プログラム、方法、およびシステム

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105046886B (zh) * 2015-08-06 2017-11-10 平安科技（深圳）有限公司 实现驾驶行为监管与提醒的方法、车辆及移动终端
EP3671481A4 (en) * 2017-10-10 2021-05-05 Hakuhodo DY Holdings Inc. INFORMATION PROCESSING SYSTEM, DATA PROVIDING SYSTEM AND RELEVANT PROCEDURE

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002048589A (ja) * 2000-08-03 2002-02-15 Tohoku Denshi Sangyo Kk 移動体の移動経路推定装置
JP2002092784A (ja) * 2000-09-14 2002-03-29 Nippon Telegr & Teleph Corp <Ntt> 交通流検出方法、移動局装置および交通流検出局装置
JP2009251758A (ja) * 2008-04-02 2009-10-29 Toyota Motor Corp 歩車間通信装置、携帯端末
JP2010226174A (ja) * 2009-03-19 2010-10-07 Ntt Docomo Inc 移動手段推定装置及び移動手段推定方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1153950A (zh) * 1995-05-31 1997-07-09 富士通株式会社 移动终端及运动体运行管理系统
JP4720770B2 (ja) * 2007-04-02 2011-07-13 トヨタ自動車株式会社 車両用情報記録システム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002048589A (ja) * 2000-08-03 2002-02-15 Tohoku Denshi Sangyo Kk 移動体の移動経路推定装置
JP2002092784A (ja) * 2000-09-14 2002-03-29 Nippon Telegr & Teleph Corp <Ntt> 交通流検出方法、移動局装置および交通流検出局装置
JP2009251758A (ja) * 2008-04-02 2009-10-29 Toyota Motor Corp 歩車間通信装置、携帯端末
JP2010226174A (ja) * 2009-03-19 2010-10-07 Ntt Docomo Inc 移動手段推定装置及び移動手段推定方法

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016018308A (ja) * 2014-07-07 2016-02-01 日本電信電話株式会社 移動手段推定装置、その動作方法およびプログラム
JP2018536223A (ja) * 2015-10-15 2018-12-06 インタートラスト テクノロジーズ コーポレイション センサ情報管理システム及び方法
JP2018059884A (ja) * 2016-10-07 2018-04-12 日本無線株式会社 物標識別装置、プログラム及び方法
JP2020520520A (ja) * 2017-05-16 2020-07-09 ケンブリッジ モバイル テレマティクス，インク．Ｃａｍｂｒｉｄｇｅ Ｍｏｂｉｌｅ Ｔｅｌｅｍａｔｉｃｓ， Ｉｎｃ． トリップの種類を識別するためのテレマティクスデータの使用
JP7295036B2 (ja) 2017-05-16 2023-06-20 ケンブリッジ モバイル テレマティクス，インク． トリップの種類を識別するためのテレマティクスデータの使用
US11713967B2 (en) * 2017-10-13 2023-08-01 JVC Kenwood Corporation Angular speed derivation device and angular speed derivation method for deriving angular speed based on output value of triaxial gyro sensor
JP2019139462A (ja) * 2018-02-09 2019-08-22 株式会社Ｓｃｒｅｅｎホールディングス 混雑状況推定方法、混雑状況推定プログラム、および混雑状況推定システム
US11030655B2 (en) 2018-11-02 2021-06-08 International Business Machines Corporation Presenting targeted content to vehicle occupants on electronic billboards
WO2021181469A1 (ja) 2020-03-09 2021-09-16 富士通株式会社 処理選択プログラム、処理選択システム、および処理選択方法
JP7395062B1 (ja) 2022-04-28 2023-12-08 楽天グループ株式会社 情報処理装置、情報処理方法、およびプログラム
JP7401716B1 (ja) 2022-04-28 2023-12-19 楽天グループ株式会社 情報処理装置、情報処理方法、プログラム、および学習モデル
JP7430835B1 (ja) 2023-04-19 2024-02-13 パシフィックコンサルタンツ株式会社 プログラム、方法、およびシステム

Also Published As

Publication number Publication date
JP5816748B2 (ja) 2015-11-18
CN104412310B (zh) 2016-11-16
IN2014DN11193A (ko) 2015-10-02
CN104412310A (zh) 2015-03-11
JPWO2013179359A1 (ja) 2016-01-14

Similar Documents

Publication Publication Date Title
JP5816748B2 (ja) 移動手段判別システム、移動手段判別装置、及び移動手段判別プログラム
Wang et al. Roadside magnetic sensor system for vehicle detection in urban environments
US10072932B2 (en) Motion detection system for transportation mode analysis
Thiagarajan et al. Cooperative transit tracking using smart-phones
US20130122928A1 (en) Systems and methods for identifying and acting upon states and state changes
Nikolic et al. Review of transportation mode detection approaches based on smartphone data
JP6139722B1 (ja) 推定装置、推定方法及び推定プログラム
Aly et al. Accurate and energy-efficient GPS-less outdoor localization
WO2015134311A1 (en) Traffic obstruction detection
CN103270780A (zh) 交通工具数据系统和方法
Aly et al. Robust and ubiquitous smartphone-based lane detection
US11162802B2 (en) Systems and methods for classifying vehicle trips
Arab et al. Magnopark-locating on-street parking spaces using magnetometer-based pedestrians' smartphones
Koster et al. Recognition and recommendation of parking places
CN112129290A (zh) 用于监测骑行设备的系统及方法
US20180035382A1 (en) Method and Device for Activating and Deactivating Geopositioning Devices in Moving Vehicles
Xiao et al. How many bumps in your city? personalized bump seeker with mobile crowdsensing
Ding et al. RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles
Chen et al. Transportation mode recognition algorithm based on multiple support vector machine classifiers
Ohashi et al. Automatic trip-separation method using sensor data continuously collected by smartphone
JP7120239B2 (ja) コンピュータプログラム、走行車線特定装置および走行車線特定システム
Ji et al. Using mobile signaling data to classify vehicles on highways in real time
WO2022081082A1 (en) Method, electronic device, and system for predicting future overspeeding
Zhu et al. Lane-level vehicular localization utilizing smartphones
Heydary et al. Automatic identification of use of public transportation from mobile sensor data

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12877687

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2014518101

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 2015/01084

Country of ref document: TR

122 Ep: pct application non-entry in european phase

Ref document number: 12877687

Country of ref document: EP

Kind code of ref document: A1